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Pattern recognition for detection of human heads in infrared images

机译：用于红外图像中人头检测的模式识别

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摘要

Human automated target recognition (ATR) capability holds important tactical value in military as well as civilian applications. Unmanned systems equipped with real-time human ATR sensors and software will serve to detect potential threats before human forces encounter them. Unattended ground stations may use human ATR in search and rescue applications, to alert rescue teams when help is necessary. The algorithm proposed in this study utilizes infrared imagery to detect people based on the radiance and shape of the human head. The algorithm works in a three-step process of segmentation, feature extraction, and classification. First, the IR image is segmented to reveal only human skin areas (e.g., arms, legs, heads). Next, three morphological features are extracted from each segmented object of interest. Finally, a classifier will use the features to determine whether the object is a head or a nonhead, based on previous algorithmic training. Two types of classifiers were tested in this study: a k-nearest-neighbor classifier and various neural networks. Results show that using a neural network classifier, 97% accuracy in head identification is possible after examining two sequential uncorrelated frames containing the same human head in different views. Tests in a desert environment at nighttime show that the majority of test subjects are detected, with few false positives.
机译：人体自动目标识别（ATR）功能在军事和民用应用中均具有重要的战术价值。配备有实时人类ATR传感器和软件的无人系统将用于在人类遇到威胁之前发现潜在威胁。无人值守的地面站可以在搜索和救援应用中使用人工ATR，在需要帮助时向救援队发出警报。本研究中提出的算法利用红外图像根据人的头部的辐射度和形状来检测人。该算法在分割，特征提取和分类的三步过程中起作用。首先，将IR图像分割成仅显示人类皮肤区域（例如，手臂，腿部，头部）。接下来，从每个感兴趣的分割对象中提取三个形态特征。最后，分类器将基于先前的算法训练，使用这些功能确定对象是头部还是非头部。在这项研究中测试了两种类型的分类器：k最近邻分类器和各种神经网络。结果表明，使用神经网络分类器，在以不同的视角检查包含相同人头的两个连续的不相关帧后，可以达到97％的头部识别精度。夜间在沙漠环境中进行的测试表明，大多数测试对象都被检测到，几乎没有假阳性。

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